Magnetic Resonance Fingerprinting (MRF), a technique recently introduced by Ma, et al., 2013, represents a paradigm shift for Magnetic Resonance Imaging (MRI). In contrast to conventional imaging strategies, where the desired image is directly encoded in the frequency domain (Bernstein, et al., 2004), in MRF a parameter map is constructed from a time-series of highly under-sampled images. Although none of the individual snap-shots in a MRF experiment yields an accurate anatomical image, the complete set captures the time dependent spin evolution. When a suitably optimized MRF sequence is used, the measured signal timecourse represents a unique “signature” identifying the underlying tissue properties in each voxel. Simulating, for a plethora of tissue and environmental parameters, the spin-dynamics induced by the sequence, a dictionary is constructed that relates each signature to the corresponding tissue properties. Finally, the desired parameter map(s) are reconstructed by identifying, for each voxel independently, the dictionary element best matching the measured signature. This map provides quantitative information about the bulk spin properties such as T1 and T2 (among others), allowing various tissues to be differentiated.
Over the years many techniques have been proposed to accelerate the acquisition process by under-sampling the frequency domain (k-space) representation of an image (Sodickson, et al., 1997; Preussmann, et al., 1999; Griswold, et al. 2002; among others). In short, these methods exploit the different receive sensitivities profiles provided by an array of receive coils to reconstruct the missing k-space data. In general, each coil has a distinct transmit- and receive-sensitivity profile (Hoult, 2000a). Traditionally, non-uniformities in the transmit-sensitivity are considered undesirable, giving rise to contrast artifacts and areas of shading in the final image. To achieve a satisfactorily uniform excitation, clinical systems typically use a large quadrature birdcage-coil for transmission. To enable parallel imaging, said systems are complemented by a set of dedicated receive array coils.
During a conventional MRF experiment, a series of images is obtained in rapid succession. To this end, following each RF pulse, a spiral readout trajectory is used to obtain a series of under-sampled images. Moreover, each of these readouts is highly under-sampled to minimize the minimum time between snapshots. Although parallel-imaging techniques could in principle be used, the images are reconstructed without the aid of said techniques. This results in strong aliasing artifacts. To minimize artifacts in the final parameter map, the orientation of the readout trajectory is changed between snapshots. Although each image will still contain strong artifacts, the aliasing patterns will be different. Following the signal measured in a single voxel through the stack of images, the incoherent artifacts add a noise like component to the measured signal evolution. It has been shown that when a large number of snapshots is acquired, e.g. 1000 images with 128×128 matrix size, the time-dependent signals can still be used to identify the underlying relaxation parameters of the tissues (Ma, et al., 2013).
In pursuit of ever more detailed anatomical images, increasingly high field strength systems have been constructed to reap the benefits of the increased signal strength. However, the Larmor frequency increases linearly with field strength, resulting in increasingly significant interactions between applied radiofrequency (RF) fields and tissue. For certain systems the RF wavelength is comparable to the dimensions of the human torso, resulting in contrast artifacts and areas with signal voids in the abdomen (Bernstein, et al., 2006). Considering research systems currently operating at 7 to 11.7 Tesla, strong RF interference effects result in extremely non-uniform transmit-sensitivity (B1+(r)) profiles (Yang, et al., 2002).
Many techniques have been proposed to mitigate spatial variations in the transmit-sensitivity profile (Hoult. 2000b; Katscher et al., 2003; Bernstein, et al., 2004; Zhu, 2004; Seheako et al., 2006; Boulant, et al., 2008; among many others). Among the most promising techniques published to date is parallel transmission (Katscher et al., 2003; Zhu, 2004). Inspired by parallel imaging, parallel transmission was introduced as a framework to capitalize on the unique sensitivity profiles provided by an array of transmits coils. Rather than accelerating the acquisition process, parallel transmission is used to reduce the duration of subject specific rf-pulses, and also to facilitate the shaping of tailored excitation profiles via interference between fields generated by distinct coil elements. These tailored excitations have been shown to provide excellent mitigation of B1+(r) non-uniformity (Setsomopop et al., 2008; Cloos et al., 2012a, 2012b). Alternatively, this technique can also be used to reduce the field of view (Schneider et al., 2013), allowing the operator to “zoom in” on a given area of interest.
In 2010, Katscher, et al. published a paper describing an attempt to use the distinct sensitivity profiles in a transmit-array to reduce the number of phase encoding steps necessary to reconstruct a conventional MR image. However, they highlight that this is extremely tedious due to the tendency of non-uniform transmit-sensitivity profiles to form contrast artifacts. To minimize these adverse effects, Katscher, et al. 2010 focuses on the diversity in the transmit-phase while striving to maintain reasonable amplitude uniformity. Moreover, the repetition time was lengthened to minimize relaxation effects. Consequently, both the acquisition speed and the obtainable contrast, both high priorities for clinical imaging, are greatly impaired.